{"id":"W4417190353","doi":"10.1016/j.compag.2025.111284","title":"PRSGNet: A robust framework for crop row detection in complex field scenarios","year":2025,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"National Key Research and Development Program of China; Special Project for Research and Development in Key areas of Guangdong Province; Earmarked Fund for Modern Agro-industry Technology Research System; National Natural Science Foundation of China; University of Manitoba","keywords":"Row; Discriminative model; Exploit; Field (mathematics); Prior probability; Pattern recognition (psychology); Row crop","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008310589,0.001792679,0.001300681,0.001234105,0.000513026,0.001222667,0.002803455,0.001841272,0.005412709],"category_scores_gemma":[0.001928577,0.0008915811,0.001076685,0.0008104603,0.0005489141,0.001506186,0.001524272,0.001348891,0.002392463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007067507,"about_ca_system_score_gemma":0.001508613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0208645,"about_ca_topic_score_gemma":0.0338946,"domain_scores_codex":[0.9996051,0.00007097622,0.00001696394,0.0001306056,0.0001196273,0.00005677004],"domain_scores_gemma":[0.999634,0.0001231089,0.0000326068,0.0000723579,0.0001105398,0.0000272221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002680207,0.0001109085,0.001136922,0.0002051497,0.0001692402,0.00020624,0.00005788124,0.68729,0.01113968,0.00653515,0.01880081,0.27408],"study_design_scores_gemma":[0.00000798992,0.00001484902,0.0001275889,0.000006971485,0.000008107444,0.00003639903,0.000007508786,0.9936591,0.00159309,0.002498148,0.002030414,0.000009804591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003284106,0.0001435105,0.9861276,0.00006712298,0.00004860511,0.00004890697,0.0005609402,0.008951399,0.0007677131],"genre_scores_gemma":[0.1449268,0.000341525,0.8451605,0.000237652,0.00009950696,0.0002341863,0.003328691,0.001461212,0.004209833],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0208645,"threshold_uncertainty_score":0.04148608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209416664290314,"score_gpt":0.2233927025296218,"score_spread":0.2112985358867187,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}